Surprising the surprises in a Six Sigma way
“Do you like surprises?â€Â Well the answer depends totally on the nature of the surprise. I mean who doesn’t like to find a bouquet of flowers from a forgetful spouse on a marriage anniversary or discover a gleaming car in one’s driveway on birthday, when least expected. Well this is the good part and good things are rarer if not unreal.
Coming to reality of things, the annoyance of a person who has just found the two sleeves of his new shirt to be of unequal lengths can be easily fathomed. Even worse, consider missing office bus and cursing yourself for faulty watch or inability to wake up on time and later finding that bus arrived early on that day. Well these are examples of unpleasant surprises also known as ‘defects’ and no one likes them.
#-Link-Snipped-#Six Sigma is a scientific methodology that helps organizations eliminates defects in their products and services. It does that by acting on the faulty processes producing those defects and making these processes reliable, efficient and cost effective too. The aim is to bring down the variation or differences in products or services a process produce to such a level whereby these variations are no more unpleasant surprises for the customer.
A typical six sigma process can follow any of the two methodologies namely DMAIC (Acronym for Define – Measure – Analyze – Improve – Control) or DFSS (Acronym for ‘Design for Six Sigma’). The methodology a Six Sigma process follows depends entirely on the objective of the process. DMAIC methodology suits more to the processes where a significant improvement is desired in the existing systems (typical examples include manufacturing processes), these systems may not be able to produce defect free products or services currently. DFSS methodology is used where a new product or service has to be designed to meet customer expectations in first go itself (processes related to marketing, design etc. are some examples).
In both the methodologies the first phase is to accurately define the problem and what is the improvement desired. The definition also includes what are the various sub-areas where improvements can be made and by how much. This is the ‘Define’ phase.
Second phase is ‘Measure’ stage. Here all the key process inputs (KPIs) and all the key process outputs (KPOs) for the process which needs improvement or redesign is identified. A 50k ft view or a ‘big picture view’ of the entire process is taken to identify various sub processes within the main process. A detailed view of each sub process is taken next. Each of the KPIs and KPOs identified should be specific and measurable. Through a funneling process which comprises use of tools such as FTA (Fault Tree Analysis), C&E Matrix (Cause and Effect Matrix), FMEA (Failure Mode Effect Analysis) etc. a smaller list of KPIs having larger impact on the problem is filtered out. For the next level of funneling to be performed in ‘Analyze ‘ stage, data related to effect of selected KPIs on KPOs is collected by experiments .
The ‘Analyze’ phase begins with the statistical analysis of the collected data using various statistical tools. The purpose here is to determine whether the effect of a particular KPI is statistically significant on a KPO or not. There is a difference between ‘significant effects’ as we use in normal day to day life and ‘statistically significant effect’ as used in Statistics. During this phase some of the KPIs found to be relevant in ‘Measure’ phase are found to be statistically insignificant. KPIs found significant in this phase are the once on which improvements shall be made.
In ‘Improve’ phase actions on the significant KPIs are taken to bring upon a significant improvement on KPOs. Future state processes are developed. Optimization using techniques such as DOE (Design of Experiments) are also used frequently to optimize the processes. For a DFSS methodology this stage is known as ‘Design’ stage whereby a new product or process is designed and optimized.
‘Control’ phase aims to establish control systems which ensure that the future processes do not deviate from their correct path and even if they deviate they are corrected in time, before they can produce any defects. For DFSS methodology this phase is called ‘Verify’.
Combining all these steps in a sequential manner many organizations have realized improved top and bottom lines, excellent brand positioning and recall in customers mind, killing competition and other tangible and intangible benefits.
Though Six Sigma methodology of bringing a huge impact in existing processes or developing innovative products and services have helped a lot of organizations, it is far from criticism. But to preserve the feel good factor about it, developed throughout this article I shall not discuss the criticisms and pitfalls here.
Coming to reality of things, the annoyance of a person who has just found the two sleeves of his new shirt to be of unequal lengths can be easily fathomed. Even worse, consider missing office bus and cursing yourself for faulty watch or inability to wake up on time and later finding that bus arrived early on that day. Well these are examples of unpleasant surprises also known as ‘defects’ and no one likes them.
#-Link-Snipped-#Six Sigma is a scientific methodology that helps organizations eliminates defects in their products and services. It does that by acting on the faulty processes producing those defects and making these processes reliable, efficient and cost effective too. The aim is to bring down the variation or differences in products or services a process produce to such a level whereby these variations are no more unpleasant surprises for the customer.
A typical six sigma process can follow any of the two methodologies namely DMAIC (Acronym for Define – Measure – Analyze – Improve – Control) or DFSS (Acronym for ‘Design for Six Sigma’). The methodology a Six Sigma process follows depends entirely on the objective of the process. DMAIC methodology suits more to the processes where a significant improvement is desired in the existing systems (typical examples include manufacturing processes), these systems may not be able to produce defect free products or services currently. DFSS methodology is used where a new product or service has to be designed to meet customer expectations in first go itself (processes related to marketing, design etc. are some examples).
In both the methodologies the first phase is to accurately define the problem and what is the improvement desired. The definition also includes what are the various sub-areas where improvements can be made and by how much. This is the ‘Define’ phase.
Second phase is ‘Measure’ stage. Here all the key process inputs (KPIs) and all the key process outputs (KPOs) for the process which needs improvement or redesign is identified. A 50k ft view or a ‘big picture view’ of the entire process is taken to identify various sub processes within the main process. A detailed view of each sub process is taken next. Each of the KPIs and KPOs identified should be specific and measurable. Through a funneling process which comprises use of tools such as FTA (Fault Tree Analysis), C&E Matrix (Cause and Effect Matrix), FMEA (Failure Mode Effect Analysis) etc. a smaller list of KPIs having larger impact on the problem is filtered out. For the next level of funneling to be performed in ‘Analyze ‘ stage, data related to effect of selected KPIs on KPOs is collected by experiments .
The ‘Analyze’ phase begins with the statistical analysis of the collected data using various statistical tools. The purpose here is to determine whether the effect of a particular KPI is statistically significant on a KPO or not. There is a difference between ‘significant effects’ as we use in normal day to day life and ‘statistically significant effect’ as used in Statistics. During this phase some of the KPIs found to be relevant in ‘Measure’ phase are found to be statistically insignificant. KPIs found significant in this phase are the once on which improvements shall be made.
In ‘Improve’ phase actions on the significant KPIs are taken to bring upon a significant improvement on KPOs. Future state processes are developed. Optimization using techniques such as DOE (Design of Experiments) are also used frequently to optimize the processes. For a DFSS methodology this stage is known as ‘Design’ stage whereby a new product or process is designed and optimized.
‘Control’ phase aims to establish control systems which ensure that the future processes do not deviate from their correct path and even if they deviate they are corrected in time, before they can produce any defects. For DFSS methodology this phase is called ‘Verify’.
Combining all these steps in a sequential manner many organizations have realized improved top and bottom lines, excellent brand positioning and recall in customers mind, killing competition and other tangible and intangible benefits.
Though Six Sigma methodology of bringing a huge impact in existing processes or developing innovative products and services have helped a lot of organizations, it is far from criticism. But to preserve the feel good factor about it, developed throughout this article I shall not discuss the criticisms and pitfalls here.
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